IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v376y2024ipas0306261924016088.html
   My bibliography  Save this article

Investigation of a high-temperature combination heat pump for lower-cost electrification in multifamily buildings

Author

Listed:
  • Kim, Junyoung
  • James, Nelson
  • Maguire, Jeff

Abstract

The development of space and water heating combination heat pumps capable of generating water temperatures high enough for convective heat emitters will enable more cost-effective and equitable decarbonization solutions for electrifying multifamily buildings. In this paper, multifamily building models and a charge-sensitive mechanistic cycle model of a combination heat pump are developed, and the system performance is predicted based on the models. Unlike other state-of-the-art residential heat pumping equipment, the modeled combination heat pump using an economized, fluid-injected variable-speed compressor can achieve higher temperature lifts of 40° – 85°C, with lower installation costs and complexity. The model predicted heating coefficient of performance (COPh) is 2.1 at an ambient temperature of −15°C with a high-temperature lift of nearly 85°C, and a seasonal coefficient of performance in heating mode (SCOPh) ranges from 2 – 4 for different locations. The system shows 30% – 90% lower CO2eq emissions over a condensing gas boiler and 9% – 13% lower projected installation costs than two separate space and water heat pumping appliances.

Suggested Citation

  • Kim, Junyoung & James, Nelson & Maguire, Jeff, 2024. "Investigation of a high-temperature combination heat pump for lower-cost electrification in multifamily buildings," Applied Energy, Elsevier, vol. 376(PA).
  • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s0306261924016088
    DOI: 10.1016/j.apenergy.2024.124225
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924016088
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.124225?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mota-Babiloni, Adrián & Navarro-Esbrí, Joaquín & Makhnatch, Pavel & Molés, Francisco, 2017. "Refrigerant R32 as lower GWP working fluid in residential air conditioning systems in Europe and the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1031-1042.
    2. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    3. Cho, Il Yong & Seo, HyeongJoon & Kim, Dongwoo & Kim, Yongchan, 2016. "Performance comparison between R410A and R32 multi-heat pumps with a sub-cooler vapor injection in the heating and cooling modes," Energy, Elsevier, vol. 112(C), pages 179-187.
    4. Mark O. McLinden & J. Steven Brown & Riccardo Brignoli & Andrei F. Kazakov & Piotr A. Domanski, 2017. "Limited options for low-global-warming-potential refrigerants," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    5. Qv, Dehu & Dong, Bingbing & Cao, Lin & Ni, Long & Wang, Jijin & Shang, Runxin & Yao, Yang, 2017. "An experimental and theoretical study on an injection-assisted air-conditioner using R32 in the refrigeration cycle," Applied Energy, Elsevier, vol. 185(P1), pages 791-804.
    6. Kim, Dongwoo & Chung, Hyun Joon & Jeon, Yongseok & Jang, Dong Soo & Kim, Yongchan, 2017. "Optimization of the injection-port geometries of a vapor injection scroll compressor based on SCOP under various climatic conditions," Energy, Elsevier, vol. 135(C), pages 442-454.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Juan Carlos Roca Reina & Johan Carlsson & Jonathan Volt & Agne Toleikyte, 2025. "Alternatives for Decarbonising High-Temperature Heating Facilities in Residential Buildings," Energies, MDPI, vol. 18(2), pages 1-19, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Dongwoo & Lee, DongChan & Lee, Minwoo & Chung, Hyun Joon & Kim, Yongchan, 2021. "Energy performance evaluation of two-phase injection heat pump employing low-GWP refrigerant R32 under various outdoor conditions," Energy, Elsevier, vol. 214(C).
    2. Kim, Dongwoo & Song, Kang Sub & Lim, Junyub & Kim, Yongchan, 2018. "Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions," Energy, Elsevier, vol. 155(C), pages 117-127.
    3. Kim, Dongwoo & Myeong, Seongryeol & Cha, Dowon & Kim, Yongchan, 2019. "Novel optimized operating strategies of two-phase injection heat pumps for achieving best performance with safe compression," Energy, Elsevier, vol. 187(C).
    4. Jung, Jongho & Jeon, Yongseok & Cho, Wonhee & Kim, Yongchan, 2020. "Effects of injection-port angle and internal heat exchanger length in vapor injection heat pumps for electric vehicles," Energy, Elsevier, vol. 193(C).
    5. Albà, C.G. & Alkhatib, I.I.I. & Llovell, F. & Vega, L.F., 2023. "Hunting sustainable refrigerants fulfilling technical, environmental, safety and economic requirements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    6. Angelo Maiorino & Manuel Gesù Del Duca & Jaka Tušek & Urban Tomc & Andrej Kitanovski & Ciro Aprea, 2019. "Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks," Energies, MDPI, vol. 12(10), pages 1-22, May.
    7. Maeng, Heegyu & Kim, Jinyoung & Kwon, Soonbum & Kim, Yongchan, 2023. "Energy and environmental performance of vapor injection heat pumps using R134a, R152a, and R1234yf under various injection conditions," Energy, Elsevier, vol. 280(C).
    8. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    9. López-Belchí, Alejandro & Illán-Gómez, Fernando, 2017. "Evaluation of a condenser based on mini-channels technology working with R410A and R32. Experimental data and performance estimate," Applied Energy, Elsevier, vol. 202(C), pages 112-124.
    10. Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
    11. Wen, Qiangyu & Zhi, Ruiping & Wu, Yuting & Lei, Biao & Liu, Shanwei & Shen, Lili, 2020. "Performance optimization of a heat pump integrated with a single-screw refrigeration compressor with liquid refrigerant injection," Energy, Elsevier, vol. 207(C).
    12. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    13. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    14. Shuxue, Xu & Yueyue, Wang & Jianhui, Niu & Guoyuan, Ma, 2020. "‘Coal-to-electricity’ project is ongoing in north China," Energy, Elsevier, vol. 191(C).
    15. Alrbai, Mohammad & Al-Dahidi, Sameer & Alahmer, Hussein & Al-Ghussain, Loiy & Al-Rbaihat, Raed & Hayajneh, Hassan & Alahmer, Ali, 2024. "Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants," Energy, Elsevier, vol. 308(C).
    16. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
    17. Sovacool, Benjamin K. & Griffiths, Steve & Kim, Jinsoo & Bazilian, Morgan, 2021. "Climate change and industrial F-gases: A critical and systematic review of developments, sociotechnical systems and policy options for reducing synthetic greenhouse gas emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    18. Jeon, Yongseok & Kim, Sunjae & Lee, Sang Hun & Chung, Hyun Joon & Kim, Yongchan, 2020. "Seasonal energy performance characteristics of novel ejector-expansion air conditioners with low-GWP refrigerants," Applied Energy, Elsevier, vol. 278(C).
    19. Wang, Bo & Chao, Yijun & Zhao, Qinyu & Wang, Haoren & Wang, Yabin & Gan, Zhihua, 2021. "A high efficiency stirling-type pulse tube refrigerator for cooling above 200 K," Energy, Elsevier, vol. 215(PB).
    20. Chung, Hyun Joon & Baek, Changhyun & Kang, Hoon & Kim, Dongwoo & Kim, Yongchan, 2018. "Performance evaluation of a gas injection CO2 heat pump according to operating parameters in extreme heating and cooling conditions," Energy, Elsevier, vol. 154(C), pages 337-345.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:376:y:2024:i:pa:s0306261924016088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.